Bayesian Estimation of Advanced Warning Time of Precipitation Emergence

Abstract

Abstract Climate models disagree on the direction of precipitation change over about half of the Earth. Current characterizations of expected change use the ensemble mean, which systematically underestimates the magnitude and overestimates the time of emergence (ToE) of precipitation change in regions of high uncertainty. We develop a new approach to estimate both ToE and the potential to update uncertainty in precipitation over time with new observations. Further, we develop two new metrics that increase the usefulness of ToE for adaptation planning. The time of confidence estimates when projections of precipitation emergence will have high confidence. Second, the advance warning time (AWT) indicates how long policymakers will have to prepare for a new precipitation regime after they know change is likely to occur. Our approach uses individual model projections that show change before averaging across models to calculate ToE. It then applies a Bayesian method to constrain uncertainty from climate model ensembles using a perfect model approach. Results demonstrate the potential for widespread and decades‐earlier precipitation emergence, with potential for end‐of‐century emergence to occur across 99% of the Earth compared to 60% in previous estimates. Our method reduces uncertainty in the direction of change across 8% of the globe. We find positive estimates of AWT across most of the Earth; however, in 34% of regions there is potential for no advanced warning before new precipitation regimes emerge. These estimates can guide adaptation planning, reducing the risk that policymakers are unprepared for precipitation changes that occur earlier than expected.

Keywords

• time of emergence
• uncertainty reduction
• precipitation trends
• Bayesian inference
• time of confidence
• advanced warning